{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"boston_housing.csv\")\n",
    "#显示前5行\n",
    "df.describe()\n",
    "# 目标y（房屋价格）的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(df[\"MEDV\"], bins=50, kde=True)\n",
    "plt.xlabel(\"Median value of owner-occupied homes\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 1. 对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。（10分\n",
    "答：对于matplotlib可以用```hist()```，对于seaborn可以用```distplot(a, bins=None, hist=True, kde=True, rug=False, fit=None, hist_kws=None, kde_kws=None, rug_kws=None, fit_kws=None, color=None, vertical=False, norm_hist=False, axlabel=None, label=None, ax=None)， sns.boxplot(data=df[\"MEDV\"])，\n",
    "sns.violinplot(data=df[\"MEDV\"])```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用hist\n",
    "plt.hist(df[\"MEDV\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20bb4538518>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用distplot\n",
    "sns.distplot(df[(\"MEDV\")], bins = 25, hist = True, kde = True, rug = False, fit = None, color = 'red', axlabel = 'Median value of owner-occupied homes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a0fbd9a240>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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,
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df[\"MEDV\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a0fbde1ac8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=df[\"MEDV\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。（10分） \n",
    "答：可以通过```plt.scatter(df['x'].values,df['y'].values)```绘图出他们之间的散点分布关系, 也可以通过DataFrame的corr()方法先计算出每对特征间的相关矩阵，然后将所得的相关矩阵传给seaborn的heatmap()方法，渲染出一个基于色彩编码的矩阵。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'crime rate')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[(\"MEDV\")].values,df[(\"CRIM\")].values)\n",
    "plt.xlabel(\"Median value of owner-occupied homes\", fontsize=12)\n",
    "plt.ylabel(\"crime rate\", fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols = df.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = df.corr()\n",
    "\n",
    "# 得到相关系数的绝对值，通常认为相关系数的绝对值大于0.5的特征为强相关\n",
    "data_corr = data_corr.abs()\n",
    "#plt.subplots(figsize=(13, 9))\n",
    "_, axes = plt.subplots(2, 1,  figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True, ax = axes[0])\n",
    "\n",
    "# Mask unimportant features,突出重要信息\n",
    "sns.heatmap(data_corr, mask=data_corr < 0.5, cbar=True, annot=True, ax = axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。（10分）\n",
    "答：如果发现特征之间有较强的相关性，我们应当只选取这些相关特征中的一个特征来训练模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 （10分），并重新训练最小二乘线性回归、岭回归、和Lasso模型（30分）。\n",
    "答： 运用最小最大缩放去量纲代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>...</th>\n",
       "      <th>RED_2</th>\n",
       "      <th>RED_3</th>\n",
       "      <th>RED_4</th>\n",
       "      <th>RED_5</th>\n",
       "      <th>RED_6</th>\n",
       "      <th>RED_7</th>\n",
       "      <th>RED_8</th>\n",
       "      <th>RED_24</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>log_MEDV</th>\n",
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       "      <td>0.782698</td>\n",
       "      <td>0.348524</td>\n",
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       "      <td>0.678082</td>\n",
       "      <td>0.842711</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.0</td>\n",
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       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.058148   0.0  0.314815  0.577505  0.641607  0.268711   \n",
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       "3  0.000293  0.00  0.053333   0.0  0.150206  0.658555  0.441813  0.448173   \n",
       "4  0.000705  0.00  0.053333   0.0  0.150206  0.687105  0.528321  0.448173   \n",
       "\n",
       "        TAX  PTRATIO    ...     RED_2  RED_3  RED_4  RED_5  RED_6  RED_7  \\\n",
       "0  0.208015      0.3    ...         0      0      0      0      0      0   \n",
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       "4  0.066794      0.6    ...         0      1      0      0      0      0   \n",
       "\n",
       "   RED_8  RED_24      MEDV  log_MEDV  \n",
       "0      0       0  0.433790  0.674359  \n",
       "1      0       0  0.378995  0.626668  \n",
       "2      0       0  0.678082  0.842711  \n",
       "3      0       0  0.648402  0.825183  \n",
       "4      0       0  0.712329  0.862159  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -*- coding:utf-8 -*-\n",
    "import sys\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"boston_housing.csv\")\n",
    "\n",
    "# 去除疑似噪声点\n",
    "df = df[df.MEDV < 50]\n",
    "\n",
    "# 从原始特征中分离输入特征x和输出y\n",
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis = 1)\n",
    "\n",
    "# 对房屋价格进行log变换\n",
    "log_y = np.log1p(y)\n",
    "\n",
    "# RAD 的含义是距离高速公路的便利指数，将它换乘离散特征编码\n",
    "X[\"RAD\"].astype(\"object\")\n",
    "X_cat = X[\"RAD\"]\n",
    "X_cat = pd.get_dummies(X_cat, prefix=\"RED\")\n",
    "\n",
    "X = X.drop(\"RAD\", axis = 1)\n",
    "\n",
    "# 特征名称， 用于保存特征工程结果\n",
    "feat_names = X.columns\n",
    "\n",
    "# 数据标准化\n",
    "mm_X = MinMaxScaler()\n",
    "mm_y = MinMaxScaler()\n",
    "mm_log_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# 对训练数据，先调用fit方法训练模型，得到模型参数；然后对训练数据和测试数据进行transform\n",
    "X = mm_X.fit_transform(X)\n",
    "\n",
    "# 对y做标准化不是必须\n",
    "# 对y标准化的好处是不同问题的w差异不大，同时正则参数的范围也有限\n",
    "y = mm_y.fit_transform(y.values.reshape(-1, 1))\n",
    "log_y = mm_y.fit_transform(log_y.values.reshape(-1, 1))\n",
    "\n",
    "fe_data = pd.DataFrame(data = X, columns = feat_names, index = df.index)\n",
    "fe_data = pd.concat([fe_data, X_cat], axis = 1, ignore_index = False)\n",
    "\n",
    "# 加上标签y\n",
    "fe_data[\"MEDV\"] = y\n",
    "fe_data[\"log_MEDV\"] = log_y\n",
    "\n",
    "#保存到结果文件\n",
    "fe_data.to_csv(\"FE_boston_housing_minmaxscaler.csv\", index = False)\n",
    "\n",
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_df = pd.read_csv(\"FE_boston_housing_minmaxscaler.csv\")\n",
    "\n",
    "# 从原始数据中分离输入特征x 和 输出特征y\n",
    "y = fe_df[\"MEDV\"]\n",
    "X = fe_df.drop([\"MEDV\", \"log_MEDV\"], axis = 1)\n",
    "\n",
    "# 特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns\n",
    "\n",
    "# 当数据量比较大时， 可用train_test_split 从训练集中分出一部分做校验集； 样本数目较少时，建议用交叉验证。\n",
    "# 在线性回归中，留一交叉验证有简便计算方式\n",
    "# 下面将训练集数据分割成训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本， 其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 33, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.6918346703206382\n",
      "The r2 score of LinearRegression on train is 0.8001507257439991\n"
     ]
    }
   ],
   "source": [
    "# 缺省参数的线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3.用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "# 看看各特征的权重系数， 系数的绝对值大小可视为该特征的重要性\n",
    "\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)\n",
    "\n",
    "# 模型评价\n",
    "# 使用r2_score 评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "from sklearn.metrics import r2_score\n",
    "# 测试集\n",
    "print(\"The r2 score of LinearRegression on test is\", r2_score(y_test, y_test_pred_lr))\n",
    "# 训练集\n",
    "print(\"The r2 score of LinearRegression on train is\", r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23fd8c9d4e0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在训练集上观察预测残差的分布， 看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize= (7, 5))\n",
    "# 自动调整子图参数，使之填充整个图像区域\n",
    "f.tight_layout()\n",
    "\n",
    "ax.hist(y_train - y_train_pred_lr, bins = 40, label = 'Residuals Linear', color = 'b', alpha = 0.5);\n",
    "ax.set_title(\"Histogram of Residuals\")\n",
    "#调整图例位置为最佳\n",
    "ax.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 观察预测值与真实值的散点图\n",
    "plt.figure(figsize = (7, 5))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([0, 1], [0, 1], '--k') # 数据已经标准化\n",
    "plt.axis('tight')\n",
    "plt.xlabel('Ture price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.e-02 1.e-01 1.e+00 1.e+01 1.e+02]\n",
      "The r2 score of RidgeCV on test is  0.6932533010598798\n",
      "The r2 score of RidgeCV on train is  0.800079018611284\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "# 1. 设置超参数（正则参数范围）\n",
    "alphas = np.logspace(-2, 2, 5)\n",
    "print(alphas)\n",
    "\n",
    "# 2. 生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "\n",
    "# 3. 模型训练\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "# 4. 预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "# 评估\n",
    "print(\"The r2 score of RidgeCV on test is \", r2_score(y_test, y_test_pred_ridge))\n",
    "print(\"The r2 score of RidgeCV on train is \", r2_score(y_train, y_train_pred_ridge))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is  0.1\n"
     ]
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas), 1))\n",
    "\n",
    "plt.plot(np.log10(ridge.alpha_) * np.ones(3), [0.01, 0.02, 0.03])\n",
    "plt.xlabel(\"log10alpha\")\n",
    "plt.ylabel(\"mse\")\n",
    "plt.show()\n",
    "\n",
    "print(\"alpha is \", ridge.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coloums</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>coef_lr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>0.440948</td>\n",
       "      <td>0.446699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>0.101971</td>\n",
       "      <td>0.104658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RED_24</td>\n",
       "      <td>0.058989</td>\n",
       "      <td>0.061897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B</td>\n",
       "      <td>0.041514</td>\n",
       "      <td>0.041508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RED_8</td>\n",
       "      <td>0.039682</td>\n",
       "      <td>0.039532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RED_7</td>\n",
       "      <td>0.032474</td>\n",
       "      <td>0.033091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RED_3</td>\n",
       "      <td>0.021768</td>\n",
       "      <td>0.021226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>0.008555</td>\n",
       "      <td>0.008079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RED_5</td>\n",
       "      <td>-0.010714</td>\n",
       "      <td>-0.011200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RED_4</td>\n",
       "      <td>-0.013318</td>\n",
       "      <td>-0.013261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RED_2</td>\n",
       "      <td>-0.018895</td>\n",
       "      <td>-0.020636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>-0.025797</td>\n",
       "      <td>-0.024599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>-0.041070</td>\n",
       "      <td>-0.041786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RED_1</td>\n",
       "      <td>-0.054392</td>\n",
       "      <td>-0.054486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RED_6</td>\n",
       "      <td>-0.055594</td>\n",
       "      <td>-0.056163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TAX</td>\n",
       "      <td>-0.106944</td>\n",
       "      <td>-0.109003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>-0.110663</td>\n",
       "      <td>-0.114821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>-0.179602</td>\n",
       "      <td>-0.180497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>-0.178385</td>\n",
       "      <td>-0.191897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>-0.321298</td>\n",
       "      <td>-0.331288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-0.345639</td>\n",
       "      <td>-0.345060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coloums  coef_ridge   coef_lr\n",
       "5        RM    0.440948  0.446699\n",
       "1        ZN    0.101971  0.104658\n",
       "20   RED_24    0.058989  0.061897\n",
       "10        B    0.041514  0.041508\n",
       "19    RED_8    0.039682  0.039532\n",
       "18    RED_7    0.032474  0.033091\n",
       "14    RED_3    0.021768  0.021226\n",
       "3      CHAS    0.008555  0.008079\n",
       "16    RED_5   -0.010714 -0.011200\n",
       "15    RED_4   -0.013318 -0.013261\n",
       "13    RED_2   -0.018895 -0.020636\n",
       "2     INDUS   -0.025797 -0.024599\n",
       "6       AGE   -0.041070 -0.041786\n",
       "12    RED_1   -0.054392 -0.054486\n",
       "17    RED_6   -0.055594 -0.056163\n",
       "8       TAX   -0.106944 -0.109003\n",
       "4       NOX   -0.110663 -0.114821\n",
       "9   PTRATIO   -0.179602 -0.180497\n",
       "0      CRIM   -0.178385 -0.191897\n",
       "7       DIS   -0.321298 -0.331288\n",
       "11    LSTAT   -0.345639 -0.345060"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看个特征的权重系数， 系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"coloums\":list(feat_names), \"coef_ridge\":list(ridge.coef_.T), \"coef_lr\": list(lr.coef_.T)})\n",
    "fs.sort_values(by=['coef_lr'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is  0.5423663543269808\n",
      "The r2 score of LassoCV on train is  0.71594433839554\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is  0.0005447049278906209\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "# 1. 设置超参数搜索范围\n",
    "#Lasso可以自动确定最大的alpha，所以另一种设置alpha的方式是设置最小的alpha值（alpha_min = alpha_max * eps） 和 超参数的数目（n_alphas），\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "\n",
    "#然后LassoCV对最小值和最大值之间在log域上均匀取值n_alphas个\n",
    "# np.logspace(np.log10(alpha_max * eps), np.log10(alpha_max),num=n_alphas)[::-1]\n",
    "\n",
    "# 生成LassoCV实例（默认超参数搜索范围）\n",
    "lasso = LassoCV()\n",
    "\n",
    "# 训练\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "# 测试\n",
    "y_test_pred = lasso.predict(X_test)\n",
    "y_train_pred = lasso.predict(X_train)\n",
    "\n",
    "# 评估\n",
    "print(\"The r2 score of LassoCV on test is \", r2_score(y_test_pred, y_test))\n",
    "print(\"The r2 score of LassoCV on train is \", r2_score(y_train_pred, y_train))\n",
    "\n",
    "# 可视化\n",
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses)\n",
    "plt.xlabel(\"log(alpha)\")\n",
    "plt.ylabel(\"mse\")\n",
    "plt.show()\n",
    "print(\"alpha is \", lasso.alpha_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。（30分）\n",
    "### 答：\n",
    "最小二乘的特点在于它考虑了所有的样本数据，它是一种无偏估计，因此噪声数据对最小二乘的影响是很大的，所以小二乘适用的场景是特征维数比较少且数据量也不大的场景。\n",
    "岭回归相比于最小二乘，它以损失部分信息，降低精度为代价来更好的拟合数据。但是因为岭回归的运算复杂度是维度相乘，所以岭回归的适用场景是特征维数不多，数据量较多的场景。\n",
    "Lasso的特点在于它引用的正则惩罚项在0处不可导，所以Lasso没有闭式解，通常用梯度下降算法进行计算。同时它还有另外一个特点，它更容易得到稀疏解。所以Lasso适用的场景是特征维数和数据量都很大的场景。\n",
    "从跑出来的结果来看， StandardScaler标准化和MinMaxScaler标准化对最小二乘使用后，两者的结果几乎没有区别。对样本进行去噪处理后（出去MEDV超过50的数据）， 三种模型对样本的拟合程度（r2_score）都变好了，但是对测试样本的拟合程度（r2_score)都变差了。对于这一点的解释我猜测原因1、去除的数据不都是噪声数据；2、样本数量较少，去除部分数据后使得样本数据更少了，估计起来也就更困难了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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